This invention relates to a control apparatus for elevators wherein a traffic demand or service condition concerning the elevators within a building as fluctuates depending upon time zones is estimated so as to control cages with the estimated value.
The traffic volume of elevators in a building (hereinbelow, termed "demand") fluctuates irregularly when closely observed within a period of one day, but presents similar aspects for similar time zones when observed over several days. In, for example, an office building, elevator passengers on their way to their office floors crowd on the first floor during a short period of time in the time zone in which they attend offices in the morning. In the first half of the lunch hour, many passengers go from the office floors to a restaurant floor, while in the latter half thereof, many passengers go from the restaurant floor and the first floor to the office floors. Further, many passengers go from the office floors to the first floor in the time zone in which they leave the offices in the evening. The volumes of traffic in the up direction and in the down direction are nearly equal in the daytime time zones other than mentioned above, while the volume of traffic becomes very small throughout the nighttime.
In order to deal with the traffic in the building changing in this manner by means of a limited number of elevators, the elevators are usually operated under group supervision. One of the important roles of the group supervision of the elevators is to assign an appropriate elevator to each hall call registered. Various assignment systems for the hall calls have been proposed. By way of example, there has been considered a system wherein, when a hall call is registered anew, it is tentatively assigned to respective elevators, and the waiting times of all hall calls, the possibility of the full capacity of passengers, etc. are predicted to calculate service evaluation values for all the cases, from among which the appropriate elevator is selected. In order to execute such predictive calculations, traffic data peculiar to each building is required.
For example, data on the number of passengers who get on and off the cage of each elevator at intermediate floors is required for predicting the possibility of the full capacity. When such traffic data which changes every moment is stored each time, an enormous memory capacity is necessitated, which is not practical. It is therefore common practice to reduce the required memory size by dividing the operating period of time in one day into several time zones and storing only the average traffic volumes of the respective time zones. Soon after the completion of the building, however, there is a high possiblity that the traffic data will change in accordance with changes in personnel organization in the building, and hence, it is difficult to obtain good traffic data with which the demand can be predicted accurately. For this reason, there has been thought out a system wherein traffic conditions in the building are detected so as to sequentially improve traffic data.
More specifically, the operating period of time in one day is divided into K time zones (hereinbelow, termed "sections"), and a time (hereinbelow, termed "boundary") by which a section k-1 and a section k are bounded is denoted by t.sub.k (k=2, 3, . . . K). Times t.sub.1 and t.sub.k+1 are the starting time and end time of the elevator operation, respectively. The average traffic volume P.sub.k (l) of the section k on the l-th day can be expressed by the following equation (1): ##EQU1##
Here, X.sub.k.sup.u (l) is a column vector of (F-1) dimensions (where F denotes the number of floors) the elements of which are the number of passengers to get on cages in the up direction at the respective floors in the time zone k of the l-th day. Similarly, X.sub.k.sup.d (l), Y.sub.k.sup.u (l) and Y.sub.k.sup.d (l) are column vectors which indicate the number of passengers to get on the cages in the down direction, the number of passengers to get off the cages in the up direction and the number of passengers to get off the cages in the down direction, respectively. The average traffic volume P.sub.k (l) (hereinbelow, termed "average demand") is measured by a passenger-number detector which utilizes load changes during the stoppage of the cages of the elevators and/or industrial television, ultrasonic wave, or the like.
First, it will be considered to sequentially correct the representative value of the average demand P.sub.k (l) of each time zone in a case where the boundary t.sub.k which is the time zone demarcating time is fixed.
It is thought that the columns {P.sub.k (1), P.sub.k (2), . . . } of the average demands occurring daily will disperse in the vicinity of a certain representative value P.sub.k. Since the magnitude of the representative value P.sub.k is unknown, it needs to be estimated by any method. In this case, there is the possibility that the magnitude itself of the representative value P.sub.k will change. The representative value is therefore predicted by taking a linear weighted average given in equations (2) and (3) below and attaching more importance to the average demand P.sub.k (l) measured latest, then to the other average demands P.sub.k (1), P.sub.k (2), . . . and P.sub.k (l-1). ##EQU2## EQU .lambda..sub.i =a(1-a).sup.l-i ( 3)
Here, P.sub.k (l) is the representative value which has been predicted from the average demands P.sub.k (1), . . . and P.sub.k (l) measured till the l-th day, and P.sub.k (0) is an initial value which is set to a suitable value and is set in advance. .lambda..sub.i denotes the weight of the average demand P.sub.k (i) measured on the i-th day, and this weight changes depending upon a parameter a. More specifically, an increase in the value of the parameter a results in an estimation in which more importance is attached to the latest measured average demand P.sub.k (l) than to the other average demands P.sub.k (1), . . . and P.sub.k (l-1), and in which the predictive representative value P.sub.k (l) quickly follows up the change of the representative value P.sub.k.
However, when the value of the parameter a is too large, it is feared that the predictive representative value will change too violently in a manner to be influenced by the random variations of daily data. Meanwhile, equations (2) and (3) can be rewritten as follows: EQU P.sub.k (l)=(1-a)P.sub.k (l-1)+a P.sub.k (l) (4) EQU P.sub.k (0)=P.sub.k (0) (5)
In accordance with the above equation (4), there is the advantage that the weighted average of equation (2) can be calculated without storing the observation values P.sub.k (i)(i=1, 2, . . . , l-1) of the average demands in the past.
In order to prevent the estimated value of the traffic demand of each time zone from being adversely affected by traffic on Sunday, a national holiday or the like different from an ordinary day or by temporarily-increasing nonregular traffic as in the case of the starting or end of a conference in a building having an assembly hall into which many people gather abruptly, there has been considered a system according to which a measured result P.sub.k (l) is not used for the estimation of the average demand when the measured result P.sub.k (l) differs greatly from the estimative value P.sub.k (l-1) of the average demand estimated till then. By way of example, the norm X of the estimated value P.sub.k (l-1) and the measured result P.sub.k (l) is calculated in accordance with equation (6) below, it is decided for the norm X.gtoreq.a constant value L that the measured result P.sub.k (l) is the measured result of the average demand on the day different from the ordinary day, and the estimative value P.sub.k (l) of the average demand according to equation (4) is not calculated. EQU X=.vertline..vertline.P.sub.k (l-1)-P.sub.k (l).vertline..vertline..sup.2 ( 6)
However, in a case where the organization of the personnel in the building has permanently greatly changed the norm X according to equation (6) always becomes X.gtoreq.the constant value L, and the measured result P.sub.k (l) of the average demand is decided to be the measured result of the average demand on the day different from the ordinary day. This has led to the drawback that the estimative value P.sub.k (l) of the new traffic demand is not calculated foreover, the predictive calculation of the waiting time, the possibility of full capacity or the like becomes erroneous, and the elevators are not group-supervised as intended.
Besides the traffic demand referred to above, such as the numbers of passengers getting on or off the cages or the numbers of hall calls; data expressive of a service condition such as waiting times on the halls, ride times in the cages, the number of times of passage due to the full capacity or the correct rate of prediction is considered as data for use in the group supervision etc. Also in case of group-supervising the elevators with the data expressive of the service condition, a similar drawback will arise.